Past Pandemics
Research question
- Estimate excess mortality for pandemics in 1890, 1918 and 2020 per district, age groups and sex
- Comparing of spatial pattern between the pandemics
- Investigate the determinants of spread in the context of different co-factors ( Urbanization, GIP per capita, etc-)
Data
- Collected and digitalized from Kaspar Staub’s team
- Russian flu: 1879 - 1895
- Spanish fl: 1908 - 1925
- Covid19 : 2020
Population
- Modern data population for all districts and all age groups and sex available
- Census 1888 and 1910 population data for all districts and age groups and sex, but 1900 and 1920 are not collect -> might a problem
- Census 1880, 1888, 1900, 1910, 1920 census data for all districts
Estimation: - Interpolation for total and each districts (census 1880, 1888, 1900, 1910, 1920)
- Calculation of age distribution for 1888 and 1910
- Take age distribution of 1888 to interpolate population between 1880 to 1900 and age distribution from 1910 to interpolate population between 1900 and 1920
- Maybe a student from Kaspar will also collect detailed census data from 1900 and 1920, would be a bit more precise then
Maps
- All districts are harmonised so that 1890, 1918 and 2020 have the same districts.
- Many districts from 1879 - 1920 have been combined so that they are the same as 2014-2020.
- Schauffhausen is only one district, as death data is only available for Schaffhausen as a whole.
- In Solothurn, the districts are merged as in 1876 - 1920.
- New shapefiles created via QGIS to have one map for all years (to make them comparable).
Preliminary Methods
- Estimation of expected mortality using Poisson GLM for total, sex and age groups
- Only yearly data available, therefor no seasonal effects etc can be taken into account
- Excess mortality is based on the trend in the previous 4 years (for 1890 only 4 years available)
- Pandemic years 1890, 1918 and 2020 are excluded to estimate expected values
- Bootstrapping to address the uncertainty in observed death counts and to provide a prediction interval (PI) for the predicted mortality(resampled N = 1000)
- Excess mortality is shown relatively in percentage (excess mortality = (pandemic year -baseline mortality)/baseline mortality
- To compare the districts the percentages are normalised, meaning for each year the quantiles are estimated, to see for example if same district have high excess mortality between the pandemics
- To find pattern and clusters: Moran’s I statistics ( LISA: Local Indicators of Spatial Association) were user mapping statistically significant local clusters.
Preliminary Results
Total
Relative yearly numbers of excess deaths
Maps
LISA (Local Indicators of Spatial Association)
Sex
Maps
LISA (Local Indicators of Spatial Association)
Age
- Only two age groups 0-69 and >=70.
- More precise age groups would lead to a lot of zeros in small districts
- Even with two age groups, zeros in some districts
Maps
LISA (Local Indicators of Spatial Association)
Sex and Age
- Interpretation with caution
Maps
Age 0 - 69 year
Age >=70 year
LISA (Local Indicators of Spatial Association)
Age 0 - 69 year 
Age >=70 year 
Next steps, Points to be discussed
- Bayesian approaches in hierarchical modelling (INLA)
- Lot of zeros, especially for age groups
- Bayesian approaches in hierarchical modelling (INLA) to investigate the spatial pattern of excess mortality per district
- Groups
- Too many age groups lead to too many zeros -> too many zeros are also a problem with INLA
- Maybe only 0-69 and >70 or 0-40, 41-69, >70
- Further Co-factors (I have to discuss with Kaspar):
- Urbanization
- Infant mortality rates as a proxy for health index
- Public health intervention for each district (canton)
- GDP per capita as proxy for SES
- Population density (population/km2)
- Proportion of children, 5–15 y (as school-age children are thought to drive influenza transmission)